Over 29 million Americans are diagnosed with diabetes and another 86 million have prediabetes, resulting in an estimated $245 billion in annual medical costs and lost work and wages (https://www.cdc.gov/features/diabetesfactsheet/). Diabetes is a complex disease that results from the combined effects of genetic and environmental factors over time. Both common and rare genetic forms of diabetes share transcriptional dysregulation of insulin-producing beta cells in pancreatic islets as a hallmark. For example, the most common form of diabetes, type 2 diabetes (T2D), has been genetically dissected with multiple genome wide association studies (GWAS) that have collectively revealed >100 independent disease and related-trait associated single nucleotide polymorphisms (SNPs). Most of these loci localize to non-coding regions and have relatively small effect sizes. Using functional genomics approaches, we and others have shown these SNPs are highly significantly enriched to overlap important transcriptional regulatory elements like stretch enhancers (SE) or enhancer clusters that are specific to pancreatic islets. More recently, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprint motifs. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Importantly, rare autosomal recessive mutations that alter DNA-contacting amino acids in the DNA binding domain of RFX6 result in Mitchell?Riley syndrome, which is characterized by neonatal diabetes. Our findings could represent a connection between rare coding variation in the islet master TF RFX6 and common noncoding variations in multiple target sites for this TF. The impact of these variations mirror the expected physiological effect, with coding variants that result in neonatal diabetes and noncoding variants that result in later-onset T2D. However, it is presently unknown how these different classes of genetic variants might interact. To help close these major gaps in knowledge, we will build mechanistic understanding of genetic variant effects on transcriptional regulation and the impact these effects could have on diabetes. We will accomplish this through integrative computational analyses of experimental measures of genome, epigenome, and transcriptome profile variation across cellular states and species coupled with novel high-throughput reporter assays to test the functional relevance of targeted genetic perturbations. The resulting increase in understanding of diabetes genetic regulatory grammars will provide a foundation for interpreting disease-relevant genetic variation and providing more precise disease predictions.